In recent years, personalized services have become increasingly indispensable to businesses that offer products for sale or rent over the Internet. Such businesses operate web sites that offer personalized recommendations to their customers. The quality of these recommendations can be important to the overall success of the business, because good recommendations can translate into increased sales and bad recommendation can annoy some customers and may even drive some customers away. Thus, determining which items to suggest to a particular customer is a non-trivial task with potentially far reaching implications. For example, a business may offer many items to choose from, and customer's are typically only willing to consider a small number of recommendations at a time before becoming annoyed, usually less than ten items.
One technique that has been adapted in recent years to address these issues is collaborative filtering, which aims at predicting personalized consumer preferences for particular items. Typically a recommendation is made to a customer based on the items previously rated by other customers who have purchased the same or similar items as the customer. For example, in a typical collaborative prediction system, the input to the system can be customer ratings on items the customers have already purchased. Predicting customer preferences for items the customers have not purchased or seen are based on observed patterns of the customer or patterns observed for customers who have purchased similar items. However, in many more situations, rating an on-line customer's interest in most products offered for sale is unknown and cannot be accurately determined by the customer's behavior. For example, a customer may be presented with a number of items, but the customer's failure to click on a link to view an item can be interpreted as the customer's negative impression of the item or for some other reason not related to the customer's negative impression of the item.
Thus, businesses that offer products for sale over the Internet continue to seek enhancements in determining which items to offer to particular customers.